Wireless communication networks such as Fourth Generation (4G, also referred to as Long Term Evolution (LTE)) networks are presently widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. However, whilst current 4G technology offers much faster data rates than its previous generations, it has limitations due to its bandwidth, scalability and number of users under individual cells.
The NR standard for 5G networks has been developed and is being rolled out to provide new functionalities including enabling the connection of many things in, for example, the Internet of Things (IoT) with low latency and very greatly increased speeds. NR builds upon today's LTE networks, expanding and improving existing coverage with the goal to facilitate enhanced mobile broadband by using 5G small cells to boost the data rates on an LTE anchor network. Consequently, the 5G Radio Access architecture is composed of LTE Evolution and an NR Access Technology operable from about 1 GHz to about 100 GHz.
MIMO antenna technology has matured for wireless communication systems and has been incorporated into wireless broadband standards such as LTE, Wi-Fi and now NR. Basically, the more antennas that the transmitter/receiver is equipped with, the greater the possible signal paths and the better the performance in terms of data rate and link reliability.
Massive MIMO also known as large-scale antenna systems, very large MIMO, hyper-MIMO and full-dimension (FD) MIMO makes a break with previous MIMO practice through the use of a very large number of service antennas (e.g. hundreds or even thousands) that are operated fully coherently and adaptively. The very large number of antennas helps by focusing the transmission and reception of signal energy into ever-smaller regions of space. This brings huge improvements in throughput and energy efficiency, in particular when combined with simultaneous scheduling of a large number of user terminals (e.g., tens or hundreds). Massive MIMO was originally envisioned for time division duplex (TDD) operation, but can be applied also in frequency division duplex (FDD) operation. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to interference and intentional jamming.
One of the major changes from a 4G or LTE network to a 5G massive MIMO mobile network (wireless) communication system is the number of antennas in each gNodeB (gNB). The number of antennas for 5G massive MIMO is typically more than 100 antennas per gNB and could be as many as thousands. As there are usually at least 100 or more antennas within a gNB, the beam width of each antenna can be made much narrower.
MIMO therefore provides a method for multiplying the capacity of a radio link using multiple transmit antennas and multiple receive antennas to exploit multipath propagation. As such, massive MIMO plays an important role in 5G networks because such networks are designed to take advantage of multipath propagation between hundreds and possibly even thousands of transmit antennas and similar numbers of receive antennas. Massive MIMO is therefore an important physical layer technology for 5G NR networks due to its capability of high spectrum and energy efficiency, high spatial resolution, and simple transceiver design. However, to take advantage of its potential gains, the acquisition of CSI is crucial, but this faces a number of challenges such as the overhead of downlink training and feedback, and the computational complexity.
CSI consists of Channel Quality Indicator (CQI), PMI, CSI-RS resource indicator (CRI), strongest layer indication (SLI), RI and/or and L1-RSRP. CSI related values are computed in real time and used to try to optimize resource scheduling and spatial multiplexing among the various UEs that are requesting service. More efficient use of resources means that a system can serve more users at once. The optimization of spatial multiplexing can largely improve the system transmission efficiency. Therefore, it is highly desirable that the values reflect as accurately as possible the quality of the wireless (RF) channel, i.e., the accuracy of the transfer of bits in each direction over the wireless (RF) channel. Constraints on the processor power available to compute these values and constraints over the length of time the values remain accurate (i.e., limits to the delay in getting a value) make it difficult to optimize resource scheduling and spatial multiplexing. The computation becomes more complex if the UE is moving, since the RF conditions will vary as the user moves.
US2017264349 discloses a method performed in a UE for establishing a CSI feedback metric. The UE is configured with a grouping of available PMIs of a codebook. The grouping comprises two or more groups each of which comprises an exclusive subset of the available PMIs. The method comprises identifying, for one or more RIs, a respective parent PMI providing the highest link quality metric, LQM, and then establishing for one or more of the identified parent PMIs a respective set of child PMIs. The method involves determining a LQM for each child PMI of the established one or more sets of child PMIs and establishing the feedback metric to be the child PMI having the highest LQM. Whilst this method involves some reduction in computational complexity, it requires a two-step PMI assessment process which still engenders more than a desired level of complexity.
US2013315284 discloses a UE which can receive N or less different data streams transmitted in parallel over N antennas. The number of different data streams actually transmitted in parallel to the UE corresponds to the transmission rank, RI. If the speed of the UE does not exceed a predetermined threshold, the UE utilizes a full-size codebook containing precoder elements for all N RIs to determine a recommended RI and PMI for use in transmitting data to the UE. Otherwise, the UE utilizes a reduced-size codebook which excludes the precoder elements for at least RI-N to determine the recommended RI and PMI where RI-N corresponds to N different data streams being transmitted in parallel over the N antennas. The UE transmits an indication of the recommended RI and PMI to a node in the network. This method switches between a full codebook and a reduced codebook based on the speed of the UE relative to a threshold speed.
CN103401594 discloses a multi-user (MU) MIMO pairing method, which comprises establishing a spatial characteristic vector table on a BS side, and pairwise calculating the correlation coefficients of space vectors in the spatial vector table to obtain a correlation coefficient matrix. The method includes obtaining the optimal matching space vector of the uplink channel estimation and extracting the spatial characteristic vector of a user channel matrix, looking up the spatial correlation coefficient matrix by a dispatcher, looking up a user of which the spatial characteristic is orthogonal for pairing, applying a pairing result to downlink MU-MIMO emission, looking up an inter-user spatial correlation coefficient matrix by the dispatcher, looking up a user of which the spatial characteristic is orthogonal for pairing, and applying a pairing result to uplink MU-MIMO emission. The calculation complexity of the inter-user spatial correlation coefficient may be reduced by use of a table look-up mode.
US2012320783 discloses a method for determining CSI for use in a wireless communications network where the RI, PMI or CQI are determined based on channel covariance estimation and the Taylor series approximation of its inverse. Furthermore, the RI and PMI are determined separately. Separately determining PMI and RI can degrade network performance.
CN101626266 discloses a method for estimating RI and PMI. The method comprises: A. calculating a self-correlation matrix A of a channel matrix H: A=HHH; B. carrying out singular value decomposition (SVD) on the matrix A: A=VΣVH, where V is a unitary matrix, and Σ is a diagonal matrix; C. confirming a rank of the matrix H according to a matrix sigma and generating RI according to the rank of the matrix H; D. confirming a precoding matrix according to the rank of the matrix H and a matrix VH and generating PMI according to the precoding matrix. However, SVD involves a significant computational load and the RI is not accurate when a small singular value is obtained.
There is therefore a need for a much less computationally complex method of deriving a CSI such as PMI and/or RI in a time efficient manner.